# TODO: 导入必要的库和模块
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pickle
from tqdm import tqdm # 添加进度条

# 加载数字数据集
digits = load_digits()

# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=42)

# 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
best_accuracy = 0
best_k = 0
best_knn = None

# 初始化一个列表以存储每个k值的准确率
accuracies = []

# 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
for k in tqdm(range(1, 41)): # 添加进度条
    knn = KNeighborsClassifier(n_neighbors=k)
    knn.fit(X_train, y_train)
    accuracy = knn.score(X_test, y_test)
    accuracies.append(accuracy)
    if accuracy > best_accuracy:
        best_accuracy = accuracy
        best_k = k
        best_knn = knn

# 将最佳KNN模型保存到二进制文件
with open('best_knn_model.pkl', 'wb') as file: # 修改文件名后缀为 .pkl
    pickle.dump(best_knn, file)

# 打印最佳准确率和相应的k值
print("Best Accuracy:", best_accuracy)
print("Best K:", best_k)


plt.plot(range(1, 41), accuracies)
plt.xlabel('K')
plt.ylabel('Accuracy')
plt.title('Accuracy vs K')
plt.axvline(x=best_k, color='red', linestyle='--')
plt.text(best_k, best_accuracy, f'({best_k}, {best_accuracy:.2f})', verticalalignment='bottom', horizontalalignment='right')
plt.savefig('accuracy_plot.pdf') 
plt.show()
